Digitizing, personalizing and improving customer interactions is critical to business growth, customer retention and market engagement. Executives want to ensure that every interaction with every customer adds value – they want to deliver the next best action for each customer, each time. And they need to deliver the next best action across a portfolio of […]
Once you have a predictive analytic model, data mining output, a machine learning result or any other analytic output, the answers to these 5 questions determine how well deployment is going to go.
Get the business understanding right! Analytics Teams know that one of their biggest challenges is effective communication and collaboration with their business partners. Projects are plagued with too many iterations to get to a solution, too many detours responding to unfocused requests, and too often the final model results in a positive analytic result that […]
Bringing Clarity to Analytics Projects with Decision Modeling: A Case Study To succeed, an analytics or data science team must effectively engage with business experts who are often inexperienced with advanced analytics, machine learning and data science. They need a framework for connecting business problems to possible analytics solutions. Decision modeling brings clarity to analytics […]
A decision modeling approach using DMN is the best practice for for scaling BRMS programs. Decision modeling address 3 key challenges of a existing BRMS program, improving traceability, sustaining business engagement and maximizing re-use while minimizing duplication.
If you are kicking off your first BRMS project, don’t start by gathering the rules! Often teams will be advised to begin their business rules project by gathering all the relevant rules, in a natural language or rulebook approach. But these rules-first approaches address issues that don’t exist with modern BRMS technology, resulting in redundant and counter-productive […]